The rapid development of modern electronic and computer technologies drives more and more complicated composition and structure of electronic equipment as well as increasingly large scale thereof. To improve system safety and reliability, higher and newer requirements are raised on circuit test and diagnosis. Analog circuits in an electronic system are prone to get wrong, and analog circuit fault diagnosis has also been a “bottleneck” that restricts the circuit industry of China. It is of great theoretical values and practical significance to develop the analog circuit industry in order to conform to the new development of modern microelectronics technologies and information technologies.
At present, an analog circuit fault model classifier based on a support vector machine has the advantages that it is suitable for small sample decision, has lower requirements on the number of learning samples and can mine the classification information hidden in data under the circumstance of limited characteristic information. But its defects are that the dimensionality of the fault characteristic vectors is too large, which will increase the difficulty of learning and training of the support vector machine. In addition, different processing methods on the training data will also affect the classification accuracy of the fault mode classifier.